McDonald's buys Dynamic Yield for machine learning–driven menu recommendations

McDonald's announced on Monday that it had acquired Dynamic Yield, a tech company specializing in machine learning–driven recommendations, for around $300 million（約480億円）. CEO Steve Easterbrook published a video explaining the rationale for the purchase. Dynamic Yield's algorithms gather customer data on behalf of companies and generate personalized recommendations—a capability similar to Amazon's product recommendation engine.

Under the new system, McDonald's drive-thru stations will use the technology to adjust what menu items they display based on real-time factors including weather, time of day, and which items are currently trending. The system will also instantly suggest and display new items based on what a customer has already ordered in that transaction. For example, on a hot day, the system might recommend a frozen treat. McDonald's press release stated the goal is to "instantly suggest and display" items based on customer order history, with the aim of getting customers to buy things they didn't know they wanted.

McDonald's is not new to this approach. The chain ran limited test runs of these recommendation techniques in 2018 and found results convincing enough to justify extending the system to the entire business. The acquisition fits into McDonald's broader modernization strategy, which has included aggressive expansion of mobile ordering and self-ordering kiosks. Easterbrook told CNBC last year that McDonald's would add kiosks to thousands of restaurants over the next couple of years, signaling the company's commitment to technology-driven operational and sales improvements.

McDonald's acquisition of Dynamic Yield represents a shift toward data-driven personalization in quick-service restaurants. The company has spent the past couple of years modernizing its operations, including aggressive investment in mobile ordering and self-ordering kiosks at thousands of locations. The Dynamic Yield purchase extends that modernization by automating the sales process itself—using machine learning to predict what individual customers are most likely to buy based on contextual signals (weather, time, their previous orders) and competitive demand (trending items).

The business model mirrors Amazon's proven cross-sell strategy, but applied to a physical restaurant environment. By adjusting what is displayed and recommended at the moment of purchase, McDonald's aims to increase order value by suggesting items customers had not planned to buy. The fact that the company is willing to spend $300 million（約480億円） on this capability, after validating the approach in 2018, signals confidence that data-driven recommendations materially lift sales in its chain.

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